1
|
Deichmann J, Bachmann S, Burckhardt MA, Szinnai G, Kaltenbach HM. Simulation-Based Evaluation of Treatment Adjustment to Exercise in Type 1 Diabetes. Front Endocrinol (Lausanne) 2021; 12:723812. [PMID: 34489869 PMCID: PMC8417413 DOI: 10.3389/fendo.2021.723812] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 07/26/2021] [Indexed: 01/26/2023] Open
Abstract
Regular exercise is beneficial and recommended for people with type 1 diabetes, but increased glucose demand and changes in insulin sensitivity require treatment adjustments to prevent exercise-induced hypoglycemia. Several different adjustment strategies based on insulin bolus reductions and additional carbohydrate intake have been proposed, but large inter- and intraindividual variability and studies using different exercise duration, intensity, and timing impede a direct comparison of their effects. In this study, we use a mathematical model of the glucoregulatory system and implement published guidelines and strategies in-silico to provide a direct comparison on a single 'typical' person on a standard day with three meals. We augment this day by a broad range of exercise scenarios combining different intensity and duration of the exercise session, and different timing with respect to adjacent meals. We compare the resulting blood glucose trajectories and use summary measures to evaluate the time-in-range and risk scores for hypo- and hyperglycemic events for each simulation scenario, and to determine factors that impede prevention of hypoglycemia events. Our simulations suggest that the considered strategies and guidelines successfully minimize the risk for acute hypoglycemia. At the same time, all adjustments substantially increase the risk of late-onset hypoglycemia compared to no adjustment in many cases. We also find that timing between exercise and meals and additional carbohydrate intake during exercise can lead to non-intuitive behavior due to superposition of meal- and exercise-related glucose dynamics. Increased insulin sensitivity appears as a major driver of non-acute hypoglycemic events. Overall, our results indicate that further treatment adjustment might be required both immediately following exercise and up to several hours later, but that the intricate interplay between different dynamics makes it difficult to provide generic recommendations. However, our simulation scenarios extend substantially beyond the original scope of each model component and proper model validation is warranted before applying our in-silico results in a clinical setting.
Collapse
Affiliation(s)
- Julia Deichmann
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- Life Science Zurich Graduate School, Zurich, Switzerland
| | - Sara Bachmann
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Marie-Anne Burckhardt
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Gabor Szinnai
- Pediatric Endocrinology and Diabetology, University Children’s Hospital Basel, and Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Hans-Michael Kaltenbach
- Department of Biosystems Science and Engineering and Swiss Institute of Bioinformatics (SIB), ETH Zurich, Basel, Switzerland
- *Correspondence: Hans-Michael Kaltenbach,
| |
Collapse
|
2
|
Zavitsanou S, Massa J, Deshpande S, Pinsker JE, Church MM, Andre C, Doyle III FJ, Michelson A, Creason J, Dassau E, Eisenberg DM. The Effect of Two Types of Pasta Versus White Rice on Postprandial Blood Glucose Levels in Adults with Type 1 Diabetes: A Randomized Crossover Trial. Diabetes Technol Ther 2019; 21:485-492. [PMID: 31225739 PMCID: PMC6708265 DOI: 10.1089/dia.2019.0109] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Background: Food choices are essential to successful glycemic control for people with diabetes. We compared the impact of three carbohydrate-rich meals on the postprandial glycemic response in adults with type 1 diabetes (T1D). Methods: We performed a randomized crossover study in 12 adults with T1D (age 58.7 ± 14.2 years, baseline hemoglobin A1c 7.5% ± 1.3%) comparing the postprandial glycemic response to three meals using continuous glucose monitoring: (1) "higher protein" pasta containing 10 g protein/serving, (2) regular pasta with 7 g protein/serving, and (3) extra-long grain white rice. All meals contained 42 g carbohydrate; were served with homemade tomato sauce, green salad, and balsamic dressing; and were repeated twice in random order. After their insulin bolus, subjects were observed in clinic for 5 h. Linear mixed effects models were used to assess the glycemic response. Results: Compared with white rice, peak glucose levels were significantly lower for higher protein pasta (-32.6 mg/dL; 95% CI -48.4 to -17.2; P < 0.001) and regular pasta (-43.2 mg/dL, 95% CI -58.7 to -27.7; P < 0.001). The difference between the two types of pastas did not reach statistical significance (-11 mg/dL; 95% CI -24.1 to 3.4; P = 0.17). Total glucose area under the curve was also significantly higher for white rice compared with both pastas (P < 0.001 for both comparisons). Conclusions: This exploratory study concluded that different food types of similar macronutrient content (e.g., rice and pasta) generate significantly different postprandial glycemic responses in persons with T1D. These results provide useful insights into the impact of food choices on and optimization of glucose control. Clinical Trial Registry: clinicaltrials.gov NCT03362151.
Collapse
Affiliation(s)
- Stamatina Zavitsanou
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Jennifer Massa
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | | | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Camille Andre
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Francis J. Doyle III
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | | | - Jamie Creason
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
- Joslin Diabetes Center, Boston, Massachusetts
| | - David M. Eisenberg
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
- Address correspondence to: David M. Eisenberg, MD, Department of Nutrition, Harvard T.H. Chan School of Public Health, 665 Huntington Avenue, Building 2, Room 337, Boston, MA 02115
| |
Collapse
|